Search results

Industry 4.0 has offered significant potential for manufacturing firms to alter and rethink their business models, production processes, strategies and objectives. Manufacturing organizations have recently undergone substantial transformation due to Industry 4.0 technologies. Hence, to successfully deploy and embed Industry 4.0 technologies in their organizational operations and practices, businesses must assess their adoption readiness. For this purpose, a multi-dimensional analytical indicator methodology has been developed to measure Industry 4.0 maturity and preparedness.

A weighted average method was adopted to assess the Industry 4.0 readiness using a case study from a steel manufacturing organization.

The result revealed that the firm ranks between Industry 2.0 and Industry 3.0, with an overall score of 2.32. This means that the organization is yet to achieve Industry 4.0 mature and ready organization.

The multi-dimensional indicator framework proposed can be used by managers, policymakers, practitioners and researchers to assess the current status of organizations in terms of Industry 4.0 maturity and readiness as well as undertake a practical diagnosis and prognosis of systems and processes for its future adoption.

Although research on Industry 4.0 maturity models has grown exponentially in recent years, this study is the first to develop a multi-dimensional analytical indicator to measure Industry 4.0 maturity and readiness.

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This study aims to review key social life cycle assessment (SLCA) themes, namely, drivers and barriers of SLCA implementation, methodology and measurement metrics, classification of initiatives to improve SLCA and customer perspectives in SLCA.

A total of 148 scientific papers extracted from the Web of Science database were used and analyzed using bibliometric and content analysis.

The findings suggest that the existing research ignores several aspects of SCLA, which impedes positive growth in topical scholarship, and the study proposes a classification of SLCA research paths to enrich future research. This study contributes positively to SLCA by further developing this area, and as such, this research is a primer to gain deeper knowledge about the state-of-the-art in SLCA as well as to foresee its future scope and challenges.

The study provides an up-to-date review of extant research pertaining to SLCA.

The purpose of this study is to assess the impact of the shadow economy on three sustainable development indicators while considering the moderating effect of the governance quality, and to highlight the non-linearity of the considered relationship.

A sample of 82 countries covering the period from 1996 to 2017. The dynamic first-differenced generalized method of moments (FD-GMM) panel threshold model is implemented to control for non-linearity.

The shadow economy hinders sustainable development in countries with low-governance quality, while the opposite result holds in countries with high-governance quality. The critical thresholds triggering the switch from one regime to another vary across the sustainable development indicators. Boosting growth requires enhancing the legal system and the economic dimension of governance, while promoting environmental quality requires the implementation and enforcement of specific environment-friendly regulations.

The study addresses non-linearity and the moderating effect of governance quality. The use of six governance indicators allows to gauge the ability of each governance dimension to curb the negative effects of the shadow economy. Considering the three objectives of sustainable development allows to identify specific policy recommendations for each of them.

This paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in industrial workstations. OpSE presents a formulation analogous to the well-known Overall Equipment Effectiveness and can be obtained as the product of three distinct indicators: Standard Compliance Effectiveness, Standards Selection Effectiveness and Design Space-usage Effectiveness.

This indicator scrutinizes how usefully floor space in workstations is used to temporarily stock materials in the form of raw materials, semi-finished products, parts and components. It is suited for analyzing fixed-position layouts as well as product layouts typical of repetitive manufacturing settings, such as assembly lines in the automotive sector. The proposed indicator leverages an appropriate loss structure that features those factors affecting floor space utilization in workstations with regard to supplying and stocking materials.

An Italian manufacturer in the field of electro-technology was used as an industrial case study for the application of the methodology. The application shows how the three indicators work in practice, the effectiveness of OpSE and the methodology as a whole, in diagnosing floor space usage inefficiencies and in properly addressing improvement actions of the internal logistics in industrial settings.

The paper scrutinizes some important Key Performance Indicators (KPIs) dealing with space usage efficiency and identifies some significant drawbacks. Then it suggests a new, inclusive structure of losses and a KPI that not only measures efficiency but also allows to identify viable countermeasures.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

This study aims to investigate the relationship between digital financial inclusion and economic growth in specific Asian countries, emphasizing the exploration of how digital financial inclusion dynamics impact gross domestic per capita income.

The study creates a digital financial inclusion composite index (DFII) by incorporating essential metrics from the Global Findex report. Economic growth is measured using Gross Domestic Product per capita income in its natural logarithmic form (LnPCI), with three control variables– employment-to-population ratio; population growth and inflation. The analysis utilizes a fixed-effect dummy variable model to examine the relationship, considering unobserved country-specific heterogeneity. 30 Asian countries have been selected for the study for the periods 2014, 2017 and 2021 based on their availability, as outlined in Table 4.

The research revealed a robust positive correlation between the Digital Financial Inclusion Index (DFII) and logarithmic GDP per capita income (LnPCI), indicating higher per capita income with enhanced digital financial inclusion. Employment and population exhibited minimal influence, whereas inflation had a notable negative effect on per capita income. Population growth showed a limited impact. The model demonstrated a high explanatory power for the dependent variable (high R-squared), and the residuals displayed low autocorrelation (Durbin–Watson of 1.96).

This study adds to the existing literature by examining the intricate connection between digital financial inclusion (DFI) and economic growth in 30 Asian countries, employing a comprehensive composite index for analysis.

The study aims to promote the use of qualitative methods in service research by investigating how these methods are reported in service journals, how the level of reporting has evolved and whether methodological reporting influences the downloads or citations received by qualitative articles.

Methodological reporting practices were identified through content analysis of 318 qualitative articles published in three major service research journals and comparison with prior methodological literature. Regression analysis was used to test how the level of methodological reporting influences article downloads and citations.

The study identifies 29 reporting practices related to 9 key methodological reporting areas. The overall level of methodological reporting in published qualitative articles has increased over time. While differences in the level of reporting between service journals persist, they are narrowing. The level of methodological reporting did not influence downloads or citations of qualitative articles.

Service scholars using qualitative methods should pay attention to methodological reporting as it can improve the chances of being published. Factors such as theoretical contributions are likely to have a greater influence on article impact than methodological reporting.

No prior study has explored methodological reporting practices across different qualitative methodologies or how reporting influences article impact. For authors, reviewers and editors, the study provides an inventory of reporting practices relevant for evaluating qualitative articles, which should lower barriers for qualitative methods in service research by providing practical guidelines on what to focus on when reporting and assessing qualitative research.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

Microfinance emerged as an essential catalyst for socio-economic development and financial inclusion to reduce poverty. Microfinance institutions cannot meet their primary objective of poverty reduction if they are not sustainable financially. With the theoretical support of profit incentive theory, this paper aims to investigate the impact of organizational structure (OS), growth outreach (average loan per borrower [ALPB] and number of active borrowers), women empowerment (percentage of women borrowers [PWB]), liquidity, leverage and cost efficiency (cost per borrower) on the financial sustainability of microfinance providers (MFPs) in India and explore the possible moderating effect of the national governance indicators (NGIs).

A financial sustainability index has been developed by using principal components analysis, including both conventional measures (return of assets and return on equity) and efficiency measures (operational self-sufficiency and financial self-sufficiency). Due to the existence of endogeneity and heteroskedasticity, this study uses two-step system generalized method of moments estimates to examine the relationships for a period of 2006 to 2018.

The finding reveals that there is a strong significant relationship between financial sustainability and its influential factors. Organizatioanl Structure, loan size, women borrowers, Gross Domestic Products and inflation enhance the financial sustainability of India’s microfinance sector. However, a number of borrowers, liquidity, leverage and operating costs negatively affect the financial sustainability of MFPs of India. The estimates demonstrate that NGIs significantly moderate the association between financial sustainability and its influential factors. The NGIs negatively affect the positive impact of Organizatioanl Structure on financial sustainability. National governance increases the positive effect of loan size (ALPB) and reduces the negative effect of a number of borrowers and leverage on the financial sustainability of MFPs of India. However, NGIs negatively affect the positive relationship between Percentage of Women Borrowers and Financial sustainability of Microfinance Providers of India.

To the best of the authors’ knowledge, this study is the first of its kind that incorporates all of the six dimensions of the National Governance Indicators (NGIs) and uses as a moderator. Secondly, a financial sustainability index has been developed for measuring the financial sustainability of Microfinance Providers (MFPs).

This study aims to explore the potential impact of country-level governance in corporate financing structures.

A two-step system generalized method of moment was used due to the endogeneity issue. The whole sample comprises 3,761 firms in five economies – China, India, Pakistan, Singapore and South Korea – from 2007 to 2016.

The results indicate that the debt option for financing is not favorable under governments with an adequate governance arrangement. However, there is a direct and significant link between country governance and equity financing because in adequate governance arrangements, the possibilities of information asymmetry are minimal and businesses consider equity a more appropriate and safer financing instrument. In contrast, firms prefer to trade-credit financing in poor governance economies, which confirms an adverse link between trade credit and adequate governance.

The country’s governance should be considered a sensitive matter when deciding about corporate financing.

This arrangement of variables has not been previously analyzed in the literature, suggesting the study’s novelty.